Optimization of Patient Health Management Mechanism Under Intelligent Medical Information System

Abstract

The establishment of a scientific and complete intelligent medical information analysis application model is of great significance to promote the application of intelligent medical information. Aiming at the deficiencies of Artificial Fish School Algorithm (AFSA) in iterative convergence speed, low optimization accuracy, and Particle Swarm Optimization (PSO) algorithm easily falling into local extremes, this paper combines AFSA and PSO algorithms. We use the fast local convergence ability of the PSO algorithm to overcome the shortcomings of the AFSA algorithm’s low solution accuracy and slow convergence speed. In the classification stage, we try to apply machine learning technology to classify the labeled feature vectors, evaluate and analyze the performance of these two machine learning algorithms in intelligent medical diagnosis auxiliary applications, and use today’s popular deep learning classification methods (i.e., intelligently optimized text classification model) and machine learning classification method to compare the classification effect, evaluate and analyze the applicability of the classification model in the auxiliary application of intelligent medical diagnosis. The experimental results show that the accuracy rate of applying the machine learning method to the judgment of the type of disease reaches more than 90%, which is fully in line with the disease judgment of the patient.